Introduction
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Get to know the data
The data contains 2 spreadsheets. First spreadsheet contains information about the borrowers’ information such as account nummber, first name, last name, zip code, total mortgage balances, total home equity balances, number of trades past due and number of home equity trades. Second spreadsheet contains information about the loans such as account number, FICO score original and current, origination date, maturity date, commitments, outstandings, day past due and APR. Certain information have been excluded because they have too many missing data, or it had zero values such as annual income, collateral value and payment amount.
Account_Number FICO_Score_Original FICO_Score_Current Commitments
1 5000 677 0 0
2 5000 677 0 0
3 5000 677 0 0
4 5000 677 0 0
5 5000 677 0 0
6 5000 677 0 0
Outstandings LTV_Original LTV_Current Day_Past_Due Origination_DTI APR
1 0 0.816 0.137 0 0.327 0.0171
2 0 0.816 0.137 0 0.327 0.0176
3 0 0.816 0.136 0 0.327 0.0181
4 0 0.816 0.141 0 0.327 0.0178
5 0 0.816 0.140 0 0.327 0.0167
6 0 0.816 0.140 0 0.327 0.0176 Total_Mortgage_Balances Total_HE_Balances
1 0 0
2 0 133829
3 0 81498
4 18691 23091
5 0 13107
6 121705 2093Column
Motivation
As a lender, Peace Bank offers clients access to money in the form of loans or lines of credit with the understanding that some customers may fail to repay it. In order to maintain a profitable business, this expected loss must be factored into the credit decision-making process by pricing the risk into the interest rate charged to each client.
Delinquency rate is a pivotal credit risk metric used to assess the quality and health of Peace Bank’s credit portfolios. As an example, a customer who has paid all prior months’ loan bills on time is considered current on their obligations. However, once that customer misses a payment for the first time they become delinquent. As the portion of the portfolio considered delinquent grows, investors and managers quickly take notice.
In this project, we are tasked with predicting which customers from this month’s pool of “current” customers will become delinquent next month. Remove Column {.tabset data-width=400} this line and the corresponding tags in your rmarkdown file if you don’t want to have several tages here!
Tag 1
Tag 2
Data Exploration
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Cleaning the dataset
Before we can start exploring the data, we first need to clean up the dataset, because there is a lot of empty columns that we need to remove and also some missing values.
Column
Credit Score Charting
First thing first, because it’s such a huge dataset, we want to clean the data so that we can better predict customers’ delinquincy.
Step 1: Using experian credit score chart, let’s have 1 for credit score higher than 800, 2 for credit score between 799 and 740, 3 for credit score between 739 and 670, 4 for credit score between 669 and 580, and 5 for credit score below 579.
Merging the two dataset
Step 2: Merge both loans and borrowers so that we only have one data set that we call df_all. The merge has been done by account number because that’s what they all have in common.
Removing accounts that have already been paid off
Step 3: Let’s use a for loop to remove the loans that have already been paid off.
Indexing the accounts
Step 4: Most customers have 6 account numbers except for a few, and for that reason, we want to use the first account number that we will call 1 to judge whether or not the customer will pay for the second account number which we will call 2. So in summary, that’s why we have indexes i+1 and i in order to check whether or not the customer will be delinquent.
Results
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Logistic regression
Let’s apply logistic regression. add a few words about lr.
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Data splitting
0 1
13932 8215 (Intercept) FICO_Score_Original2 FICO_Score_Original3
0.4010855 1.0140830 0.9791683
FICO_Score_Original4 FICO_Score_Original5 FICO_Score_Current2
1.0170244 1.1451981 1.0403782
FICO_Score_Current3 FICO_Score_Current4 FICO_Score_Current5
1.1475646 1.0982923 1.2152653
Commitments Outstandings LTV_Original
1.0000440 0.9999603 0.8926434
LTV_Current Day_Past_Due Origination_DTI
1.5153732 0.9995273 0.9844571
APR Total_Mortgage_Balances Total_HE_Balances
1.2796113 1.0000000 1.0000003 Results
0=good and 1=bad
0 1
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1929 0.57633559 0.42366441
1943 0.63002648 0.36997352
1944 0.65566679 0.34433321
1945 0.66031723 0.33968277
1946 0.63421406 0.36578594
1947 0.64633893 0.35366107
1973 0.63254740 0.36745260
1974 0.62894574 0.37105426
1975 0.63920899 0.36079101
1976 0.61995088 0.38004912
1977 0.63397432 0.36602568
1997 0.59995392 0.40004608
1998 0.58620253 0.41379747
1999 0.59652094 0.40347906
2000 0.60074681 0.39925319
2001 0.55517777 0.44482223
2015 0.63170327 0.36829673
2016 0.65109014 0.34890986
2017 0.59447099 0.40552901
2018 0.59687480 0.40312520
2019 0.59868549 0.40131451
2027 0.60408792 0.39591208
2028 0.64361768 0.35638232
2029 0.61802945 0.38197055
2030 0.62792042 0.37207958
2031 0.63842758 0.36157242
2063 0.62176962 0.37823038
2064 0.60456748 0.39543252
2065 0.54463481 0.45536519
2066 0.55248445 0.44751555
2067 0.59043479 0.40956521
2087 0.39019321 0.60980679
2088 0.32233385 0.67766615
2089 0.35168889 0.64831111
2090 0.33715910 0.66284090
2091 0.28111674 0.71888326
2093 0.64308590 0.35691410
2094 0.64416162 0.35583838
2095 0.63967363 0.36032637
2096 0.64885861 0.35114139
2097 0.65286921 0.34713079
2111 0.62452574 0.37547426
2112 0.63003061 0.36996939
2113 0.61214516 0.38785484
2114 0.63332043 0.36667957
2115 0.64571760 0.35428240
2183 0.64371790 0.35628210
2184 0.67312767 0.32687233
2185 0.58488832 0.41511168
2186 0.59720824 0.40279176
2187 0.60431946 0.39568054
2237 0.66673145 0.33326855
2238 0.63702750 0.36297250
2239 0.63285334 0.36714666
2240 0.65125237 0.34874763
2241 0.62137528 0.37862472
2243 0.60485310 0.39514690
2244 0.61565569 0.38434431
2245 0.66530500 0.33469500
2246 0.63074315 0.36925685
2247 0.63046081 0.36953919
2255 0.62055434 0.37944566
2256 0.62725437 0.37274563
2257 0.64071986 0.35928014
2258 0.66217384 0.33782616
2259 0.64232128 0.35767872
2261 0.63535074 0.36464926
2262 0.65397073 0.34602927
2263 0.64861425 0.35138575
2264 0.66855880 0.33144120
2265 0.64560687 0.35439313
2279 0.59324362 0.40675638
2280 0.63415536 0.36584464
2281 0.59023238 0.40976762
2282 0.62016882 0.37983118
2283 0.62091625 0.37908375
2303 0.65994480 0.34005520
2304 0.66856811 0.33143189
2305 0.66370490 0.33629510
2306 0.66910818 0.33089182
2307 0.62291965 0.37708035
2321 0.62400905 0.37599095
2322 0.66995366 0.33004634
2323 0.68540857 0.31459143
2324 0.68383780 0.31616220
2325 0.67029515 0.32970485
2351 0.64945419 0.35054581
2352 0.63828276 0.36171724
2353 0.64075089 0.35924911
2354 0.64246850 0.35753150
2355 0.66363123 0.33636877
2357 0.66367691 0.33632309
2358 0.63790739 0.36209261
2359 0.66560104 0.33439896
2360 0.65120310 0.34879690
2361 0.65339326 0.34660674
2423 0.66332101 0.33667899
2424 0.66123597 0.33876403
2425 0.62635897 0.37364103
2426 0.62889819 0.37110181
2427 0.63967400 0.36032600
2507 0.58093053 0.41906947
2508 0.60583549 0.39416451
2509 0.59411600 0.40588400
2510 0.63738110 0.36261890
2511 0.63488764 0.36511236
2537 0.63965720 0.36034280
2538 0.64695188 0.35304812
2539 0.62735578 0.37264422
2540 0.60510211 0.39489789
2541 0.62867561 0.37132439
2543 0.61063425 0.38936575
2544 0.59120826 0.40879174
2545 0.59230922 0.40769078
2546 0.61516173 0.38483827
2547 0.58073850 0.41926150
2549 0.61741998 0.38258002
2550 0.66088958 0.33911042
2551 0.63660881 0.36339119
2552 0.65095310 0.34904690
2553 0.65441614 0.34558386
2573 0.64629133 0.35370867
2574 0.64594179 0.35405821
2575 0.65132309 0.34867691
2576 0.65148098 0.34851902
2577 0.65062923 0.34937077
2579 0.67597261 0.32402739
2580 0.66574378 0.33425622
2581 0.54999074 0.45000926
2582 0.57173097 0.42826903
2583 0.57899212 0.42100788
2657 0.66405201 0.33594799
2658 0.64544099 0.35455901
2659 0.66977065 0.33022935
2660 0.66728532 0.33271468
2661 0.64681764 0.35318236
2687 0.60482089 0.39517911
2688 0.63112684 0.36887316
2689 0.61281088 0.38718912
2690 0.62704546 0.37295454
2691 0.63383258 0.36616742
2783 0.66661346 0.33338654
2784 0.66176695 0.33823305
2785 0.63585367 0.36414633
2786 0.64038301 0.35961699
2787 0.64061025 0.35938975
2789 0.61254191 0.38745809
2790 0.60461853 0.39538147
2791 0.62607784 0.37392216
2792 0.65851874 0.34148126
2793 0.64121163 0.35878837
2807 0.66404635 0.33595365
2808 0.62177699 0.37822301
2809 0.63919799 0.36080201
2810 0.66941094 0.33058906
2811 0.66016497 0.33983503
2831 0.63820498 0.36179502
2832 0.63845720 0.36154280
2833 0.66168865 0.33831135
2834 0.64945816 0.35054184
2835 0.64771569 0.35228431
2867 0.48074011 0.51925989
2868 0.44336267 0.55663733
2869 0.48856838 0.51143162
2870 0.45022462 0.54977538
2871 0.44022051 0.55977949
2903 0.58647269 0.41352731
2904 0.55909976 0.44090024
2905 0.55043062 0.44956938
2906 0.60614657 0.39385343
2907 0.60964527 0.39035473
2963 0.64034049 0.35965951
2964 0.66671224 0.33328776
2965 0.64509184 0.35490816
2966 0.67247546 0.32752454
2967 0.65227584 0.34772416
3005 0.53759106 0.46240894
3006 0.56216648 0.43783352
3007 0.59096244 0.40903756
3008 0.57395069 0.42604931
3009 0.54597544 0.45402456
3035 0.57957769 0.42042231
3036 0.55232618 0.44767382
3037 0.54808141 0.45191859
3038 0.62898591 0.37101409
3039 0.56818923 0.43181077
3083 0.65952215 0.34047785
3084 0.64042413 0.35957587
3085 0.67184869 0.32815131
3086 0.65237628 0.34762372
3087 0.65483358 0.34516642
3131 0.60737452 0.39262548
3132 0.64186863 0.35813137
3133 0.64781211 0.35218789
3134 0.65675413 0.34324587
3135 0.65863202 0.34136798
3137 0.63481985 0.36518015
3138 0.63186847 0.36813153
3139 0.63865149 0.36134851
3140 0.64613931 0.35386069
3141 0.63709513 0.36290487
3149 0.66939660 0.33060340
3150 0.66576356 0.33423644
3151 0.67405087 0.32594913
3152 0.67564185 0.32435815
3153 0.64996595 0.35003405
3173 0.62978044 0.37021956
3174 0.64221908 0.35778092
3175 0.53850121 0.46149879
3176 0.56031604 0.43968396
3177 0.57876760 0.42123240
3203 0.64765168 0.35234832
3204 0.58027865 0.41972135
3205 0.56656267 0.43343733
3206 0.56396618 0.43603382
3207 0.60187174 0.39812826
3209 0.50011804 0.49988196
3210 0.48361431 0.51638569
3211 0.62778352 0.37221648
3212 0.59638818 0.40361182
3213 0.65789910 0.34210090
3221 0.66340208 0.33659792
3222 0.66104875 0.33895125
3223 0.66863263 0.33136737
3224 0.67221421 0.32778579
3225 0.67963334 0.32036666
3233 0.58252584 0.41747416
3234 0.62438755 0.37561245
3235 0.64878346 0.35121654
3236 0.60516713 0.39483287
3237 0.57997586 0.42002414
3245 0.62271150 0.37728850
3246 0.62403054 0.37596946
3247 0.56995713 0.43004287
3248 0.58964220 0.41035780
3249 0.60891620 0.39108380
3781 0.65894895 0.34105105
3782 0.65693523 0.34306477
3783 0.67738992 0.32261008
3784 0.67898554 0.32101446
3785 0.63506415 0.36493585
3793 0.66797534 0.33202466
3794 0.66631290 0.33368710
3795 0.66539264 0.33460736
3796 0.67037947 0.32962053
3797 0.66344868 0.33655132
3829 0.54316987 0.45683013
3830 0.54415357 0.45584643
3831 0.55705645 0.44294355
3832 0.55134285 0.44865715
3833 0.53496376 0.46503624
3871 0.57167754 0.42832246
3872 0.53184976 0.46815024
3873 0.52730492 0.47269508
3874 0.57839643 0.42160357
3875 0.56125617 0.43874383
3919 0.65189616 0.34810384
3920 0.64952406 0.35047594
3921 0.66839060 0.33160940
3922 0.67341672 0.32658328
3923 0.68126223 0.31873777
3925 0.62283479 0.37716521
3926 0.62502562 0.37497438
3927 0.62427533 0.37572467
3928 0.62440751 0.37559249
3929 0.62955367 0.37044633
3931 0.65264139 0.34735861
3932 0.65189660 0.34810340
3933 0.66503024 0.33496976
3934 0.66025448 0.33974552
3935 0.64056780 0.35943220
3949 0.63363770 0.36636230
3950 0.64500308 0.35499692
3951 0.61104870 0.38895130
3952 0.64591222 0.35408778
3953 0.57939465 0.42060535
3955 0.66802536 0.33197464
3956 0.67171392 0.32828608
3957 0.66701552 0.33298448
3958 0.67760724 0.32239276
3959 0.63910385 0.36089615
3961 0.64536053 0.35463947
3962 0.62328903 0.37671097
3963 0.62841136 0.37158864
3964 0.63374926 0.36625074
3965 0.64947841 0.35052159
3991 0.43042378 0.56957622
3992 0.43593023 0.56406977
3993 0.52402018 0.47597982
3994 0.47349059 0.52650941
3995 0.59319001 0.40680999
4081 0.63613278 0.36386722
4082 0.63734096 0.36265904
4083 0.63364813 0.36635187
4084 0.63588511 0.36411489
4085 0.63735770 0.36264230
4099 0.62601141 0.37398859
4100 0.63020508 0.36979492
4101 0.62949945 0.37050055
4102 0.63905743 0.36094257
4103 0.63989934 0.36010066
4111 0.65977454 0.34022546
4112 0.67016709 0.32983291
4113 0.66782386 0.33217614
4114 0.67591277 0.32408723
4115 0.67089401 0.32910599
4202 0.62208710 0.37791290
4203 0.65954895 0.34045105
4204 0.62478779 0.37521221
4205 0.64856270 0.35143730
4206 0.55466490 0.44533510
4220 0.67906865 0.32093135
4221 0.68226517 0.31773483
4222 0.63778075 0.36221925
4223 0.63754731 0.36245269
4224 0.60704914 0.39295086
4238 0.64731970 0.35268030
4239 0.66645200 0.33354800
4240 0.62612293 0.37387707
4241 0.63724074 0.36275926
4242 0.65784130 0.34215870
4244 0.67598868 0.32401132
4245 0.67622243 0.32377757
4246 0.67982899 0.32017101
4247 0.67947476 0.32052524
4248 0.68143085 0.31856915
4262 0.67233003 0.32766997
4263 0.68381197 0.31618803
4264 0.63510167 0.36489833
4265 0.64122985 0.35877015
4266 0.67776289 0.32223711
4268 0.64595709 0.35404291
4269 0.65057661 0.34942339
4270 0.66412499 0.33587501
4271 0.65632286 0.34367714
4272 0.66827656 0.33172344
4322 0.67933897 0.32066103
4323 0.68138747 0.31861253
4324 0.63572707 0.36427293
4325 0.63680213 0.36319787
4326 0.68317489 0.31682511
4346 0.63122335 0.36877665
4347 0.63623854 0.36376146
4348 0.63497776 0.36502224
4349 0.62412356 0.37587644
4350 0.66236140 0.33763860
4364 0.66928906 0.33071094
4365 0.67455725 0.32544275
4366 0.62296594 0.37703406
4367 0.64413354 0.35586646
4368 0.68631147 0.31368853
4376 0.67944259 0.32055741
4377 0.67616095 0.32383905
4378 0.67825038 0.32174962
4379 0.67512974 0.32487026
4380 0.68550721 0.31449279
4406 0.66117212 0.33882788
4407 0.62721522 0.37278478
4408 0.47362820 0.52637180
4409 0.44745979 0.55254021
4410 0.48363443 0.51636557
4412 0.62777886 0.37222114
4413 0.66791422 0.33208578
4414 0.68969907 0.31030093
4415 0.66537752 0.33462248
4416 0.65925753 0.34074247
4418 0.67258571 0.32741429
4419 0.67617300 0.32382700
4420 0.62538225 0.37461775
4421 0.63500845 0.36499155
4422 0.60963732 0.39036268
4466 0.64194102 0.35805898
4467 0.64217938 0.35782062
4468 0.62008718 0.37991282
4469 0.62315912 0.37684088
4470 0.61504707 0.38495293
4490 0.60651877 0.39348123
4491 0.60309041 0.39690959
4492 0.63287538 0.36712462
4493 0.64210784 0.35789216
4494 0.62624180 0.37375820
4520 0.68364214 0.31635786
4521 0.68175211 0.31824789
4522 0.58942881 0.41057119
4523 0.59562248 0.40437752
4524 0.55520809 0.44479191
4526 0.66561691 0.33438309
4527 0.67190065 0.32809935
4528 0.62638757 0.37361243
4529 0.63930564 0.36069436
4530 0.67332852 0.32667148
4544 0.60648415 0.39351585
4545 0.59860926 0.40139074
4546 0.61639153 0.38360847
4547 0.62055988 0.37944012
4548 0.64401861 0.35598139
4568 0.65943874 0.34056126
4569 0.64725578 0.35274422
4570 0.63571099 0.36428901
4571 0.63960925 0.36039075
4572 0.65102864 0.34897136
4586 0.65309283 0.34690717
4587 0.65080121 0.34919879
4588 0.63763030 0.36236970
4589 0.63628471 0.36371529
4590 0.66195698 0.33804302
4592 0.62351589 0.37648411
4593 0.61424068 0.38575932
4594 0.56732861 0.43267139
4595 0.57484883 0.42515117
4596 0.57086122 0.42913878
4604 0.66292511 0.33707489
4605 0.65351019 0.34648981
4606 0.64007544 0.35992456
4607 0.63977578 0.36022422
4608 0.64047865 0.35952135
4640 0.63050904 0.36949096
4641 0.59108042 0.40891958
4642 0.56728355 0.43271645
4643 0.59852516 0.40147484
4644 0.60673492 0.39326508
4652 0.62747650 0.37252350
4653 0.64377076 0.35622924
4654 0.64856257 0.35143743
4655 0.66000545 0.33999455
4656 0.65624273 0.34375727
4664 0.67225830 0.32774170
4665 0.65576100 0.34423900
4666 0.62570209 0.37429791
4667 0.62655143 0.37344857
4668 0.68110486 0.31889514
4688 0.58573179 0.41426821
4689 0.56183238 0.43816762
4690 0.51991046 0.48008954
4691 0.49205124 0.50794876
4692 0.53574442 0.46425558
4706 0.67313558 0.32686442
4707 0.67425446 0.32574554
4708 0.64354753 0.35645247
4709 0.63801425 0.36198575
4710 0.68024789 0.31975211
4718 0.64078559 0.35921441
4719 0.67629814 0.32370186
4720 0.66249020 0.33750980
4721 0.66780907 0.33219093
4722 0.65314491 0.34685509
4742 0.65103996 0.34896004
4743 0.63328932 0.36671068
4744 0.65507860 0.34492140
4745 0.64925864 0.35074136
4746 0.61711684 0.38288316
4748 0.46662216 0.53337784
4749 0.54681558 0.45318442
4750 0.53379691 0.46620309
4751 0.56500532 0.43499468
4752 0.50283976 0.49716024
4814 0.61587419 0.38412581
4815 0.61704518 0.38295482
4816 0.62556488 0.37443512
4817 0.62783693 0.37216307
4818 0.63644175 0.36355825
4826 0.64500983 0.35499017
4827 0.66554470 0.33445530
4828 0.67613763 0.32386237
4829 0.68633274 0.31366726
4830 0.66889015 0.33110985
4892 0.67869745 0.32130255
4893 0.66590354 0.33409646
4894 0.65628175 0.34371825
4895 0.64881051 0.35118949
4896 0.64286048 0.35713952
4922 0.65874689 0.34125311
4923 0.64506370 0.35493630
4924 0.60674178 0.39325822
4925 0.61852116 0.38147884
4926 0.64819703 0.35180297
4934 0.63224065 0.36775935
4935 0.64062282 0.35937718
4936 0.60607053 0.39392947
4937 0.61381467 0.38618533
4938 0.62563592 0.37436408
4952 0.66730698 0.33269302
4953 0.65758297 0.34241703
4954 0.62507843 0.37492157
4955 0.62981961 0.37018039
4956 0.67718500 0.32281500
4970 0.63890062 0.36109938
4971 0.65916366 0.34083634
4972 0.63260365 0.36739635
4973 0.63074831 0.36925169
4974 0.57639078 0.42360922
4988 0.62147454 0.37852546
4989 0.58829858 0.41170142
4990 0.53569499 0.46430501
4991 0.57686695 0.42313305
4992 0.63340148 0.36659852
5018 0.55237034 0.44762966
5019 0.56472532 0.43527468
5020 0.57033980 0.42966020
5021 0.61104514 0.38895486
5022 0.61475392 0.38524608
5030 0.64757430 0.35242570
5031 0.66069212 0.33930788
5032 0.63642939 0.36357061
5033 0.62749441 0.37250559
5034 0.68196216 0.31803784
5036 0.65590238 0.34409762
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5039 0.61514493 0.38485507
5040 0.66462488 0.33537512
5102 0.65158979 0.34841021
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5105 0.62579633 0.37420367
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5150 0.62754615 0.37245385
5151 0.63459138 0.36540862
5152 0.56101884 0.43898116
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5154 0.56341131 0.43658869
5180 0.63144541 0.36855459
5181 0.62944670 0.37055330
5182 0.61800518 0.38199482
5183 0.63012537 0.36987463
5184 0.61501153 0.38498847
5186 0.63532190 0.36467810
5187 0.63112072 0.36887928
5188 0.63372465 0.36627535
5189 0.63113666 0.36886334
5190 0.65873895 0.34126105
5252 0.49764890 0.50235110
5253 0.43073650 0.56926350
5254 0.46530070 0.53469930
5255 0.56067496 0.43932504
5256 0.46030988 0.53969012
5264 0.62897640 0.37102360
5265 0.63580075 0.36419925
5266 0.62851870 0.37148130
5267 0.62223225 0.37776775
5268 0.62897229 0.37102771
5294 0.46510691 0.53489309
5295 0.40527299 0.59472701
5296 0.39571701 0.60428299
5297 0.54040254 0.45959746
5298 0.49285381 0.50714619
5300 0.67601705 0.32398295
5301 0.67020715 0.32979285
5302 0.62679058 0.37320942
5303 0.63293694 0.36706306
5304 0.66781051 0.33218949
5348 0.54826785 0.45173215
5349 0.57954700 0.42045300
5350 0.51864136 0.48135864
5351 0.59883800 0.40116200
5352 0.58480080 0.41519920
5432 0.66174596 0.33825404
5433 0.63039245 0.36960755
5434 0.65124590 0.34875410
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29441 0.68881272 0.31118728
29479 0.61374359 0.38625641
29480 0.64103580 0.35896420
29481 0.68401448 0.31598552
29482 0.65693488 0.34306512
29483 0.68183039 0.31816961
29491 0.66391364 0.33608636
29492 0.62021474 0.37978526
29493 0.59125918 0.40874082
29494 0.63970440 0.36029560
29495 0.65590172 0.34409828
29509 0.66068211 0.33931789
29510 0.66425830 0.33574170
29511 0.65363284 0.34636716
29512 0.65007255 0.34992745
29513 0.57830446 0.42169554
29521 0.55922997 0.44077003
29522 0.55926934 0.44073066
29523 0.65924321 0.34075679
29524 0.59857844 0.40142156
29525 0.59590312 0.40409688
29551 0.62217205 0.37782795
29552 0.52587964 0.47412036
29553 0.54158951 0.45841049
29554 0.56222593 0.43777407
29555 0.51812053 0.48187947
29611 0.69017114 0.30982886
29612 0.68489804 0.31510196
29613 0.69428943 0.30571057
29614 0.69372266 0.30627734
29615 0.69470605 0.30529395
29617 0.50990004 0.49009996
29618 0.60100369 0.39899631
29619 0.53027538 0.46972462
29620 0.56992214 0.43007786
29621 0.55824686 0.44175314
29629 0.48407974 0.51592026
29630 0.56708389 0.43291611
29631 0.60230254 0.39769746
29632 0.55179118 0.44820882
29633 0.53931666 0.46068334
29653 0.66355291 0.33644709
29654 0.66528066 0.33471934
29655 0.68947879 0.31052121
29656 0.67248736 0.32751264
29657 0.66835817 0.33164183
29665 0.67506905 0.32493095
29666 0.67551258 0.32448742
29667 0.66471252 0.33528748
29668 0.67325332 0.32674668
29669 0.66425274 0.33574726
29701 0.58812405 0.41187595
29702 0.61487867 0.38512133
29703 0.63329108 0.36670892
29704 0.65063577 0.34936423
29705 0.60704027 0.39295973
29713 0.57114531 0.42885469
29714 0.54293151 0.45706849
29715 0.60595585 0.39404415
29716 0.63479304 0.36520696
29717 0.55705722 0.44294278
29737 0.66025378 0.33974622
29738 0.67604004 0.32395996
29739 0.66092572 0.33907428
29740 0.66958897 0.33041103
29741 0.63852592 0.36147408
29743 0.65013025 0.34986975
29744 0.67088770 0.32911230
29745 0.68001440 0.31998560
29746 0.65128178 0.34871822
29747 0.65353048 0.34646952
29749 0.62069506 0.37930494
29750 0.63887147 0.36112853
29751 0.60891931 0.39108069
29752 0.59280956 0.40719044
29753 0.64946518 0.35053482
29779 0.58937175 0.41062825
29780 0.65099210 0.34900790
29781 0.62414413 0.37585587
29782 0.57346944 0.42653056
29783 0.56326558 0.43673442
29863 0.47891698 0.52108302
29864 0.53311968 0.46688032
29865 0.54218657 0.45781343
29866 0.53791666 0.46208334
29867 0.60325785 0.39674215
29869 0.68640395 0.31359605
29870 0.68597088 0.31402912
29871 0.68474640 0.31525360
29872 0.68686748 0.31313252
29873 0.68681985 0.31318015
29887 0.62362557 0.37637443
29888 0.63941244 0.36058756
29889 0.58558855 0.41441145
29890 0.55050582 0.44949418
29891 0.55708549 0.44291451
29905 0.58418437 0.41581563
29906 0.54566187 0.45433813
29907 0.60552527 0.39447473
29908 0.59525068 0.40474932
29909 0.64028330 0.35971670
29917 0.67603489 0.32396511
29918 0.64101868 0.35898132
29919 0.66996993 0.33003007
29920 0.65678274 0.34321726
29921 0.64718466 0.35281534
29929 0.68094621 0.31905379
29930 0.68278117 0.31721883
29931 0.67980072 0.32019928
29932 0.69164868 0.30835132
29933 0.34129189 0.65870811[1] 0.6294691Confusion Matrix and Statistics
Reference
Prediction 0 1
0 3366 1900
1 152 120
Accuracy : 0.6295
95% CI : (0.6166, 0.6422)
No Information Rate : 0.6352
P-Value [Acc > NIR] : 0.8179
Kappa : 0.0199
Mcnemar's Test P-Value : <2e-16
Sensitivity : 0.95679
Specificity : 0.05941
Pos Pred Value : 0.63919
Neg Pred Value : 0.44118
Prevalence : 0.63525
Detection Rate : 0.60780
Detection Prevalence : 0.95088
Balanced Accuracy : 0.50810
'Positive' Class : 0
Diagnostic Regression
Column
We start by computing an example of logistic regression model for predicting the probability of customers will make a payment the next month based on regressors.
Column
Building a logistic regression model
mod_fit <- train(response~., data=training, method=“glm”, family=“binomial”)
probabilities <- predict(mod_fit, type = “response”) predicted.classes <- ifelse(probabilites > 0.5, “pos”, “neg”) head(predicted.classes)
Column
Linearity assumption
mydata <- mod_fit %>% dplyr::select_if(is.numeric) predictors <- colnames(mydata)
mydata <- mydata %>% mutate(logit = log(probabilities/(1-probabilities))) %>% gather(key = “predictors”, value = “predictor.value”, -logit)
Column
Create the scatter plots
ggplot(mydata, aes(logit, predictor.value))+ geom_point(size = 0.5, alpha = 0.5) + geom_smooth(method = “loess”) + theme_bw() + facet_wrap(~predictors, scales = “free_y”)